Bibliographic record
Abstract
Climate is integral to the concept of terroir. With anthropogenic climate change, the terroir of the world’s winegrowing regions is changing, and will continue to change for decades or centuries. The clearest signal of this shift comes from the earlier harvests of winegrapes over the last several decades with harvests 2–3 weeks earlier in France and other regions. These earlier harvests have reshaped the climatic profile under which berries ripen, leading to wines with higher alcohol and shifted phenolic and aromatic attributes. But these shifts also hint at a major way to adapt viticulture to climate change—through matching variety phenology to the current and future climates of established winegrowing regions. Here I show how variety phenology—the timing of major growth and reproductive events including budburst, flowering, veraison and harvest—is a critical component of terroir and one that is becoming increasingly mismatched due to climate change. I outline how growers and researchers alike can leverage current and new data to help develop a framework to shift varieties with climate change, and discuss how this could help build a more dynamic definition of terroir—one that embraces the challenges, and potential opportunities, of the Anthropocene.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.006 | 0.021 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".